Bibliographic record
Abstract
This paper explores how TEMU, an emerging global e-commerce marketplace, shapes merchant-consumer relations through its “Refund Without Return” (hereafter refund-without-return) policy and associated penalty policy. Using digital ethnography, I analyze approximately 100 posts and videos sampled from merchant and customer content on TikTok, YouTube, Reddit, Meta platforms, and Chinese online forums between 2022 and 2024, together with TEMU’s public documents and media coverage. Thematic analysis based on platform capitalism and digital labour theory shows that the refund-without-return policy and the five-fold penalty mechanism are, in fact, a unilateral agreement that shifts financial risks and operational burdens to small businesses, leading to unstable income, heavy debt, and widespread anxiety and exhaustion. Meanwhile, social media communities spread “tips,” “secrets,” and success stories about how to take advantage of refund policies, packaging these practices as smart consumer behaviour and “happy shopping,” therefore diffusing individual responsibility. Bringing these strands together, I conceptualize TEMU’s refund-without-return policy as a dual exploitation mechanism, in which platform governance and online consumer behaviour jointly erode merchants’ bargaining power and economic security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".